Cloud as a Story - Vunvulea Radu

 Cloud AI-Native delivery means turning AI from a basic demonstration into a scalable platform. This requires modern cloud infrastructure, up-to-date & well-organized data, engineering practices suitable for operating AI at scale, and processes to ensure AI is used safely and responsibly. So, what does a company actually need to do to make this work? Build platforms, not just projects A company must design and build reusable foundations. Reliable frameworks have to support products and teams, rather than creating isolated projects. This means the company must be able to create reference architectures, standard templates, clear approaches, and clear processes for how teams work. Security, cost control, and operational monitoring must be built into the platform design at the start, not added later. Modernise applications, not just move them A company must migrate from lift-and-shift systems to cloud-native ones. This calls for skills in refactoring, containerisation, breaking mon...

 By Phase 5, most organisations have working systems. Applications are refactored, data modernised, AI integrated, and governance established. It is tempting to think the journey is over. AI-Native platforms are not classic IT. You don’t deploy and forget. Models drift, prompts evolve, embeddings go stale, costs shift, and user expectations change quickly. This is why Phase 5 is a continual Build–Run–Evolve cycle. In the image I use for this phase, the cycle is simple: Build → Run → Evolve. Behind this simplicity lies a serious message: AI requires automation and operational discipline on par with engineering. Build: Focus on making delivery repeatable, not dependent on individual effort. In AI projects, ‘heroic delivery’ is common: one team member deploys the model, another fixes the pipeline, and a few keep the platform alive. This does not scale. Build means we standardise how we build and release everything: infrastructure, applications, data pipelines, prompts, models, policie...

 During the first three phases of the AI-Native journey, we focus on major engineering tasks. We refactor applications to be cloud-native, update data for RAG and real-time context, and make AI workloads a core part of the platform. At this point, many organizations are excited because the technology is working. Demos are impressive, agents respond, and models help users. However, a new challenge appears: intelligence also brings risk. Without proper governance, the same AI that creates value can also cause harm. This leads to Phase 4, where governance becomes as important as architecture and data. It is not just a compliance task, but a practical way to enable safe AI scaling. 1. Data security and access control become more critical than ever GenAI systems reveal information in ways that traditional applications never could. While a report or dashboard only shows what it is meant to, a GenAI assistant can combine data and produce unexpected answers. If access controls are weak, se...

 In Phase 1, we refactor applications to become cloud-native. In Phase 2, we modernise data so GenAI can work with trusted, fresh, and governed information. But even after these two phases, many organisations still treat AI like a side experiment. A small team builds a model, deploys it somewhere, and the rest of the platform stays the same. This is where real adoption stalls. Phase 3 is the moment when AI stops being a PoC and becomes a native workload inside the platform. It means training, inference, embeddings, retrieval, and orchestration are treated like first-class production components, with the same standards you apply to any critical service: scalability, security, observability, and lifecycle management. 1. Make inference a standard platform capability Most GenAI systems fail in production not because the model is weak, but because inference is not engineered as a real service should be. Inference presents latency, burst traffic, and cost challenges. Deploying it as a VM...

 As Microsoft’s AI platform has grown, so have the terms describing it, which can cause confusion about Foundry, Azure AI Foundry, and Azure AI Services. These tools support different aspects of AI adoption and are designed to work together. One way to look at the difference is that Azure AI Services offer specific AI features, while Microsoft Foundry gives you the platform and structure to use those features effectively as your needs grow. Azure AI Services (Foundry Tools): focused AI capabilities Azure AI Services are ready-made APIs that provide specific AI functions such as analyzing images and documents, recognizing and generating speech, understanding language, translating text, or connecting to large language models. These services are well-suited for scenarios where an application needs a clearly defined AI feature. They can be provisioned individually, integrated quickly, and scaled independently. This makes them ideal for feature enhancements, proofs of concept, and solut...

In Phase 1, we reshape applications so they can scale, change, and integrate intelligence. But even with a perfect cloud-native architecture, GenAI will still fail if the data foundation is weak. This is why Phase 2 is always about data modernisation. In simple words: GenAI is only as good as the data you feed it, and most organisations today still feed it ‘yesterday’. Many companies have data, but it is fragmented. Some sits in SQL databases, some in file shares, some in SharePoint, some in CRM, and much knowledge is hidden in PDFs, tickets, and Teams messages. When you build GenAI on top of this chaos, the result is inconsistent answers and low trust. And then people say, ‘GenAI doesn’t work for us’. Usually, it is not a model problem. It is a data problem. Below is how I see Phase 2, in a practical, structured way. Move from batch to near real-time data flows Traditional data platforms mostly use batch ETL. A pipeline runs overnight, updates the warehouse, and reports are accurate t...